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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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对于声音场重建的微分物理.

Samuel A Verburg1, Efren Fernandez-Grande2, Peter Gerstoft1

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科学领域:

  • 声学 声学 在声学方面
  • 计算物理 计算物理
  • 机器学习 机器学习

背景情况:

  • 声场重建从有限的空间观测中估计声场.
  • 像物理信息神经网络 (PINNs) 这样的传统方法将物理纳入损失函数中.
  • 严重的低采样给准确的声音场重建带来了挑战.

研究的目的:

  • 为强大的声音场重建引入差分物理方法.
  • 在数据稀缺条件下提高准确性和趋同.
  • 在网络培训期间,强化物理作为强有力的约束.

主要方法:

  • 用神经网络近似波方程初始条件.
  • 使用微分算子的可微分数值解析器.
  • 为了改善重建,纳入一种促进稀缺性的约束.

主要成果:

  • 拟议的方法通过强制物理作为强制约束来实现稳定的网络训练.
  • 在极端数据稀缺的情况下,证明了成功的声场重建.
  • 在准确性和收性方面表现优于传统的基于物理的神经网络.

结论:

  • 微分物理方法为声音场重建提供了稳定有效的方法.
  • 这种技术在不足样本的场景中显著提高了性能.
  • 它为现有的基于物理的神经网络方法提供了一个强大的替代方案.